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/** KEYIN：输入kv数据对中key的数据类型* VALUEIN：输入kv数据对中value的数据类型* KEYOUT：输出kv数据对中key的数据类型* VALUEOUT：输出kv数据对中value的数据类型* 数据类型为Writable类型*/public static class MyMapper extends Mapper<KEYIN, VALUEIN, KEYOUT, VALUEOUT>{ // Context为MapReduce上下文，在Map中通常用于将数据处理结果输出 public void map(KEYIN key, VALUEIN value, Context context) throws IOException, InterruptedException {// Map功能的实现 } }
public static class MyReducer extends Reducer<KEYIN,VALUEIN,KEYOUT,VALUEOUT> {// 这里reduce方法的输入的Value值是可迭代Iterable类型，因为Reduce阶段会将Key值相同的数据放置在一起 public void reduce(KEYIN key, Iterable<VALUEIN> values, Context context ) throws IOException, InterruptedException { // Reduce功能的实现 }}

public static void main(String[] args) throws Exception {// 配置类 Configuration conf = new Configuration(); // 创建MapReduce Job实例 Job job = Job.getInstance(conf, "Job Name"); // 为MapReduce作业设置必要的配置 // 设置main函数所在的入口类 job.setJarByClass(WordCount.class); // 设置Map和Reduce实现类，并指定Combiner job.setMapperClass(MyMapper.class); job.setCombinerClass(MyReducer.class); job.setReducerClass(IntSumReducer.class); // 设置结果数据的输出类 job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); // 设置结果数据的输入和输出路径 FileInputFormat.addInputPath(job, new Path(args[0])); FileOutputFormat.setOutputPath(job, new Path(args[1])); // 作业运行，并输出结束标志 System.exit(job.waitForCompletion(true) ? 0 : 1);}

job.setNumReduceTasks(int)

// 将Hadoop当前用户切换为hdfs，进行访问授权export HADOOP_USER_NAME=hdfs// 在HDFS中创建作业输入目录hadoop fs -mkdir -p /tmp/mr/data/wc_input// 在本地创建词频统计文件vim wordcount.txt// 文本内容如下hello hadoophello hdfshello yarnhello mapreduce// 将wordcount.txt上传到作业输入目录hadoop fs -put wordcount.txt /tmp/mr/data/wc_input

import java.io.IOException;import java.util.StringTokenizer;
import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.fs.Path;import org.apache.hadoop.io.IntWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.Job;import org.apache.hadoop.mapreduce.Mapper;import org.apache.hadoop.mapreduce.Reducer;import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
public class WordCount {
/** 实现Mapper，文件的每一行数据会执行一次map运算逻辑* 因为输入是文件，会将处理数据的行数作为Key，这里应为LongWritable，设置为Object也可以；Value类型为Text：每一行的文件内容* Mapper处理逻辑是将文件中的每一行切分为单词后，将单词作为Key，而Value则设置为1，<Word,1>* 因此输出类型为Text，IntWritable*/public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable>{
// 事先定义好Value的值，它是IntWritable，值为1 private final static IntWritable one = new IntWritable(1); // 事先定义好Text对象word，用于存储提取出来的每个单词 private Text word = new Text();
 public void map(Object key, Text value, Context context ) throws IOException, InterruptedException { // 将文件内容的每一行数据按照空格拆分为单词 StringTokenizer itr = new StringTokenizer(value.toString()); // 遍历单词，处理为<word,1>的Key-Value形式，并输出（这里会调用上下文输出到buffer缓冲区） while (itr.hasMoreTokens()) { word.set(itr.nextToken()); context.write(word, one); } }}
/** 实现Reducer* 接收Mapper的输出，所以Key类型为Text，Value类型为IntWritable* Reducer的运算逻辑是Key相同的单词，对Value进行累加* 因此输出类型为Text，IntWritable，只不过IntWritable不再是1，而是最终累加结果*/public static class IntSumReducer extends Reducer<Text,IntWritable,Text,IntWritable> { // 预先定义IntWritable对象result用于存储词频结果 private IntWritable result = new IntWritable();
 public void reduce(Text key, Iterable<IntWritable> values, Context context ) throws IOException, InterruptedException { int sum = 0; // 遍历key相同单词的value值，进行累加 for (IntWritable val : values) { sum += val.get(); } result.set(sum); // 将结果输出 context.write(key, result); }}
// 实现Main方法public static void main(String[] args) throws Exception { Configuration conf = new Configuration(); Job job = Job.getInstance(conf, "word count"); job.setJarByClass(WordCount.class); job.setMapperClass(TokenizerMapper.class); job.setCombinerClass(IntSumReducer.class); job.setReducerClass(IntSumReducer.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); FileInputFormat.addInputPath(job, new Path(args[0])); FileOutputFormat.setOutputPath(job, new Path(args[1])); System.exit(job.waitForCompletion(true) ? 0 : 1);}}

export HADOOP_CLASSPATH=${JAVA_HOME}/lib/tools.jarhadoop com.sun.tools.javac.Main WordCount.javajar cf wc.jar WordCount*.class

hadoop jar wc.jar WordCount /tmp/mr/data/wc_input /tmp/mr/data/wc_output

hadoop fs -cat /tmp/mr/data/wc_output/part-r-*